TO DO: - add CI’s

Prep

Let’s load a couple random-ish gamlss models and the dataframe they’re built on

## GAMLSS-RS iteration 1: Global Deviance = 1218118 
## GAMLSS-RS iteration 2: Global Deviance = 1218033 
## GAMLSS-RS iteration 3: Global Deviance = 1218022 
## GAMLSS-RS iteration 4: Global Deviance = 1218017 
## GAMLSS-RS iteration 5: Global Deviance = 1218017 
## GAMLSS-RS iteration 6: Global Deviance = 1218017 
## GAMLSS-RS iteration 7: Global Deviance = 1218017 
## GAMLSS-RS iteration 8: Global Deviance = 1218017 
## GAMLSS-RS iteration 9: Global Deviance = 1218017 
## GAMLSS-RS iteration 10: Global Deviance = 1218017 
## GAMLSS-RS iteration 11: Global Deviance = 1218017 
## GAMLSS-RS iteration 12: Global Deviance = 1218017 
## GAMLSS-RS iteration 13: Global Deviance = 1218017

Call the script holding the plot functions we want to test:

source("plotting_functions.R")

Test

Set-up Functions

These functions are called from the plotting functions.

sim.data() - takes the dataframe you built your GAMLSS model on and creates a 2 new dfs with simulated data (male vs female participants) across the age-range, assigning fs_version and study values to whatever is most common in the original df. Expects input df to have log_age, fs_version, and study. Also preps x-axis labels and defines which centiles you’ll be plotting.

Returns object sim, which is a list of objects.

sim <- sim.data(cn_df)
names(sim)
## [1] "ageRange"            "dataToPredictM"      "dataToPredictF"     
## [4] "tickMarks_log"       "tickLabels_log"      "tickMarks_unscaled" 
## [7] "tickLabels_unscaled" "desiredCentiles"

centile_predict() - predicts centiles based on df simulated by sim.data() using the predictAll() and qGG() functions. Calculates centiles, 50th centile peak values, and age at peaks separately on male and female dfs and returns each, as well as an averaged effect across sexes.

Takes GAMLSS model obj and objects returned from sim.data(). Returns object pred, which is a list of objects.

pred <- centile_predict(sGMV.re, sim$dataToPredictM, sim$dataToPredictF, sim$ageRange, sim$desiredCentiles)
## new prediction 
## new prediction 
## new prediction 
## new prediction
names(pred)
##  [1] "fanCentiles"   "fanCentiles_M" "fanCentiles_F" "peak"         
##  [5] "peak_age"      "peak_M"        "peak_age_M"    "peak_F"       
##  [9] "peak_age_F"    "M_mu"          "M_sigma"       "M_nu"         
## [13] "F_mu"          "F_sigma"       "F_nu"

centile_predict.corrected() - same as centile_predict but corrects out the estimated effects of fs_version (from mu term) and study (from mu & sigma terms) parameters.

Expects fs_version to be a fixed effect and study to be a random effect fit using re() function!!!

pred.cor <- centile_predict.corrected(sGMV.re, sim$dataToPredictM, sim$dataToPredictF, sim$ageRange, sim$desiredCentiles)
## new prediction 
## new prediction 
## new prediction 
## new prediction
names(pred.cor)
##  [1] "fanCentiles"   "fanCentiles_M" "fanCentiles_F" "peak"         
##  [5] "peak_age"      "peak_M"        "peak_age_M"    "peak_F"       
##  [9] "peak_age_F"    "M_mu"          "M_sigma"       "M_nu"         
## [13] "F_mu"          "F_sigma"       "F_nu"

GGalt.variance - copied from Simon’s Nature paper repo

var <- GGalt.variance(pred$M_mu, pred$M_sigma, pred$M_nu)

Plotting

makeCentileFan() - basic centile fan plotting that averages across sex and predicts on Mode(fs_version) and Mode(study) of original data the gamlss was modeled on. Expects GAMLSS model, phenotype being modeled, and the name of the original df.

age_transformed parameter set to TRUE or FALSE

makeCentileFan(GMV.int, "GMV", cn_df, TRUE, "sex")
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6)

makeCentileFan(GMV.int, "GMV", cn_df, FALSE, "sex")
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6)

makeCentileFan(sGMV.re, "sGMV", cn_df, TRUE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

makeCentileFan(sGMV.re, "sGMV", cn_df, FALSE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

makeCentileFan_sex_overlay() - same as makeCentileFan but with separate centile lines for males and females

makeCentileFan_sex_overlay(GMV.int, "GMV", cn_df, FALSE, "sex")
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6)

makeCentileFan_sex_overlay(GMV.int, "GMV", cn_df, TRUE, "sex")
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6)

makeCentileFan_sex_overlay(sGMV.re, "sGMV", cn_df, FALSE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

makeCentileFan_sex_overlay(sGMV.re, "sGMV", cn_df, TRUE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

makeCentileFan.corrected() - centile fan plot that averages across sexes, correcting for fs_version and study effects in both centiles and data points plotted.

Expects fs_version to be a fixed effect and study to be a random effect fit using re() function!!!

age_transformed parameter set to TRUE or FALSE

makeCentileFan.corrected(sGMV.re, "sGMV", cn_df, TRUE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

#makeCentileFan(sGMV.re, "sGMV", cn_df, TRUE, "sex")
makeCentileFan.corrected(sGMV.re, "sGMV", cn_df, FALSE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

makeCentileFan_sex_overlay.corrected() - same as makeCentileFan.corrected but with separate centile lines for males and females

makeCentileFan_sex_overlay.corrected(sGMV.re, "sGMV", cn_df, TRUE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

makeCentileFan_sex_overlay.corrected(sGMV.re, "sGMV", cn_df, FALSE, "sex")
## new prediction 
## new prediction 
## new prediction 
## new prediction

plot.gamlss.var() - plots variance from predicted GAMLSS model separately for males and females.

age_transformed parameter set to TRUE or FALSE

plot.gamlss.var(sGMV.re, "sGMV", cn_df, TRUE)
## new prediction 
## new prediction 
## new prediction 
## new prediction

plot.gamlss.var(GMV.int, "GMV", cn_df, FALSE)
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6)

correct.points() - function used within site/fs_version corrected plotting to account for these parameter’s estimate’s effects on each data point. Expects re() to be used when fitting random effect of site. Can also be called on it’s own. Returns new df with each subject’s phenotype corrected.

plot_df <- correct.points(sGMV.re, "GMV", cn_df)
summary(plot_df)
##       GMV              sGMV            WMV            Ventricles    
##  Min.   : 74583   Min.   :14132   Min.   :  49733   Min.   :   811  
##  1st Qu.:465826   1st Qu.:52392   1st Qu.: 411303   1st Qu.: 11296  
##  Median :510770   Median :56277   Median : 451791   Median : 17351  
##  Mean   :519429   Mean   :56374   Mean   : 453583   Mean   : 21542  
##  3rd Qu.:565140   3rd Qu.:60497   3rd Qu.: 495940   3rd Qu.: 27358  
##  Max.   :898654   Max.   :89344   Max.   :1064862   Max.   :175416  
##                                                                     
##     session       participant         run           study          
##  1      :58764   Min.   :    1   Min.   :1.000   Length:62447      
##  2      : 1766   1st Qu.:21374   1st Qu.:1.000   Class :character  
##  3      : 1013   Median :41051   Median :1.000   Mode  :character  
##  ses-BL :  111   Mean   :40829   Mean   :1.025                     
##  ses-v0 :   89   3rd Qu.:61084   3rd Qu.:1.000                     
##  ses-v06:   73   Max.   :80991   Max.   :8.000                     
##  (Other):  631                                                     
##     fs_version       age_days         sex         dx           country     
##  Custom  :  674   Min.   :  291   Female:31646   CN:62447   UK     :30081  
##  FS53    : 3571   1st Qu.: 5393   Male  :30801              USA    :20734  
##  FS6_T1  :18219   Median :19273                             France : 1816  
##  FS6_T1T2:39335   Mean   :15698                             China  : 1268  
##  FSInfant:  648   3rd Qu.:24321                             India  : 1193  
##                   Max.   :36504                             Germany: 1036  
##                                                             (Other): 6319  
##         site          age_yrs           log_age           TBV         
##  11025    :17617   Min.   : 0.7967   Min.   :2.464   Min.   : 136632  
##  11027    : 7429   1st Qu.:14.7664   1st Qu.:3.732   1st Qu.: 917810  
##  11026    : 3985   Median :52.7659   Median :4.285   Median : 993084  
##  1        : 2469   Mean   :42.9796   Mean   :4.075   Mean   : 994554  
##  MRi-Share: 1341   3rd Qu.:66.5878   3rd Qu.:4.386   3rd Qu.:1072189  
##  PNC      : 1217   Max.   :99.9425   Max.   :4.562   Max.   :1706546  
##  (Other)  :28389                                                      
##     log_TBV         log_GMV         log_sGMV        log_WMV     
##  Min.   :5.136   Min.   :4.873   Min.   :4.150   Min.   :4.697  
##  1st Qu.:5.963   1st Qu.:5.668   1st Qu.:4.719   1st Qu.:5.614  
##  Median :5.997   Median :5.708   Median :4.750   Median :5.655  
##  Mean   :5.994   Mean   :5.710   Mean   :4.748   Mean   :5.651  
##  3rd Qu.:6.030   3rd Qu.:5.752   3rd Qu.:4.782   3rd Qu.:5.695  
##  Max.   :6.232   Max.   :5.954   Max.   :4.951   Max.   :6.027  
##                                                                 
##  log_Ventricles     site_int          pheno_adjust      fs_effect       
##  Min.   :2.909   Min.   :-0.315826   Min.   : 60252   Min.   :-0.26998  
##  1st Qu.:4.053   1st Qu.:-0.009192   1st Qu.:428767   1st Qu.: 0.07411  
##  Median :4.239   Median : 0.008215   Median :470435   Median : 0.07865  
##  Mean   :4.250   Mean   : 0.001359   Mean   :481721   Mean   : 0.07444  
##  3rd Qu.:4.437   3rd Qu.: 0.008215   3rd Qu.:523826   3rd Qu.: 0.07865  
##  Max.   :5.244   Max.   : 0.213378   Max.   :840912   Max.   : 0.10626  
## 

Experimental

Log-Log(TBV) correction

This function takes log(phenotype) ~ log(TBV) models and plots log(pheno) by age. It predicts on simulated data where for each age, log_TBV is the mean log_TBV (or roughly imputed mean) for that sex at that given age. Hence the “experimental” label, since, as you can see, it’s pretty rough.

## GAMLSS-RS iteration 1: Global Deviance = -324689.2 
## GAMLSS-RS iteration 2: Global Deviance = -325130.6 
## GAMLSS-RS iteration 3: Global Deviance = -325139.9 
## GAMLSS-RS iteration 4: Global Deviance = -325140.3 
## GAMLSS-RS iteration 5: Global Deviance = -325140.5 
## GAMLSS-RS iteration 6: Global Deviance = -325140.6 
## GAMLSS-RS iteration 7: Global Deviance = -325140.7 
## GAMLSS-RS iteration 8: Global Deviance = -325140.7 
## GAMLSS-RS iteration 9: Global Deviance = -325140.7 
## GAMLSS-RS iteration 10: Global Deviance = -325140.8 
## GAMLSS-RS iteration 11: Global Deviance = -325140.8 
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## GAMLSS-RS iteration 13: Global Deviance = -325140.8 
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## GAMLSS-RS iteration 16: Global Deviance = -325140.7 
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## GAMLSS-RS iteration 22: Global Deviance = -325140.6 
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## GAMLSS-RS iteration 56: Global Deviance = -325140 
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## GAMLSS-RS iteration 66: Global Deviance = -325139.9 
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## GAMLSS-RS iteration 94: Global Deviance = -325139.7 
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## GAMLSS-RS iteration 119: Global Deviance = -325139.6 
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makeCentileFan_sex_overlay.logTBV(log_wmv_refit, "log_WMV", cn_df, TRUE, "sex")
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6) 
## new prediction 
## New way of prediction in random()  (starting from GAMLSS version 5.0-6)